Are A2C or A3C suitable for episodic tasks where the reward is delivered only at the end of the episode?
My understanding of the main idea behind A2C / A3C is that we run small segments of an episode to estimate the return using a trainable value function to compensate for the unseen final steps of the episode.


This seems fairly accurate. The important thing to note is that the trainable value function is trained to predict values (specifically, advantage values of state-action pairs in the case of A2C / A3C, where the first A stands for "advantage"). These value estimates can intuitively be understood as estimates of long-term (discounted) rewards, they're not just short-term rewards.

So yes, initially when the agent only observes a reward at the end of a long trajectory, only state-action pairs close to the end will receive credit for that reward. For example, when using $n$-step returns, approximately only the last $n$ state-action pairs receive credit. However, in the next episode, that longer-term reward will already become "visible" in the form of an advantage value prediction when you're still $n$ steps away from the end, and then that update can again get propagated back $n$ steps further into the history of state-action pairs.

My explanation above is very informal... there are all kinds of nuances that I skipped over. Use of function approximation is likely to speed up the propagation of reward observations through the space of state-action pairs even more, and of course in reality things won't be as "clean" as getting the propagation to get $n$ steps further in the next episode in comparison to the previous episode, since selected actions and random state transitions can be different... but hopefully it gets the idea across.